mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-02-10 21:42:37 +08:00
Merge branch 'master' into issue-1608-mask-composite-error-on-nonsquare-masks
This commit is contained in:
Sammy Franklin
commit
f12a453312
@ -1,6 +1,6 @@
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ComfyUI
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=======
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A powerful and modular stable diffusion GUI and backend.
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The most powerful and modular stable diffusion GUI and backend.
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-----------
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@ -94,8 +94,8 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins
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```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.4.2```
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This is the command to install the nightly with ROCm 5.6 that supports the 7000 series and might have some performance improvements:
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```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm5.6```
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This is the command to install the nightly with ROCm 5.7 that supports the 7000 series and might have some performance improvements:
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```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm5.7```
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### NVIDIA
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@ -688,7 +688,7 @@ class UniPC:
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x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
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else:
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x_t_ = (
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expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dimss) * x
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expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
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- expand_dims(sigma_t * h_phi_1, dims) * model_prev_0
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)
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if x_t is None:
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@ -598,7 +598,7 @@ KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral"
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"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
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"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm"]
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def ksampler(sampler_name):
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def ksampler(sampler_name, extra_options={}):
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class KSAMPLER(Sampler):
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def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
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extra_args["denoise_mask"] = denoise_mask
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@ -627,7 +627,7 @@ def ksampler(sampler_name):
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elif sampler_name == "dpm_adaptive":
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samples = k_diffusion_sampling.sample_dpm_adaptive(model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback, disable=disable_pbar)
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else:
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samples = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
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samples = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **extra_options)
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return samples
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return KSAMPLER
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@ -152,7 +152,9 @@ class VAE:
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sd = comfy.utils.load_torch_file(ckpt_path)
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if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
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sd = diffusers_convert.convert_vae_state_dict(sd)
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self.first_stage_model.load_state_dict(sd, strict=False)
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m, u = self.first_stage_model.load_state_dict(sd, strict=False)
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if len(m) > 0:
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print("Missing VAE keys", m)
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if device is None:
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device = model_management.vae_device()
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@ -181,7 +183,7 @@ class VAE:
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steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
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pbar = comfy.utils.ProgressBar(steps)
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encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.vae_dtype).to(self.device) - 1.).sample().float()
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encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).sample().float()
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samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
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samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
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samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
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@ -200,7 +202,7 @@ class VAE:
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pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu")
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for x in range(0, samples_in.shape[0], batch_number):
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samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
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pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu().float()
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pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).cpu().float() + 1.0) / 2.0, min=0.0, max=1.0)
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except model_management.OOM_EXCEPTION as e:
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print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
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pixel_samples = self.decode_tiled_(samples_in)
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@ -43,6 +43,62 @@ class KarrasScheduler:
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sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
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return (sigmas, )
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class ExponentialScheduler:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
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"sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
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}
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}
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RETURN_TYPES = ("SIGMAS",)
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CATEGORY = "_for_testing/custom_sampling"
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FUNCTION = "get_sigmas"
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def get_sigmas(self, steps, sigma_max, sigma_min):
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sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max)
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return (sigmas, )
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class PolyexponentialScheduler:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
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"sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
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"rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
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}
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}
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RETURN_TYPES = ("SIGMAS",)
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CATEGORY = "_for_testing/custom_sampling"
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FUNCTION = "get_sigmas"
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def get_sigmas(self, steps, sigma_max, sigma_min, rho):
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sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
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return (sigmas, )
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class VPScheduler:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), #TODO: fix default values
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"beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
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"eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}),
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}
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}
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RETURN_TYPES = ("SIGMAS",)
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CATEGORY = "_for_testing/custom_sampling"
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FUNCTION = "get_sigmas"
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def get_sigmas(self, steps, beta_d, beta_min, eps_s):
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sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s)
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return (sigmas, )
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class SplitSigmas:
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@classmethod
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def INPUT_TYPES(s):
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@ -58,7 +114,7 @@ class SplitSigmas:
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def get_sigmas(self, sigmas, step):
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sigmas1 = sigmas[:step + 1]
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sigmas2 = sigmas[step + 1:]
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sigmas2 = sigmas[step:]
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return (sigmas1, sigmas2)
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class KSamplerSelect:
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@ -77,6 +133,53 @@ class KSamplerSelect:
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sampler = comfy.samplers.sampler_class(sampler_name)()
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return (sampler, )
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class SamplerDPMPP_2M_SDE:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"solver_type": (['midpoint', 'heun'], ),
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"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
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"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
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"noise_device": (['gpu', 'cpu'], ),
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}
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}
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RETURN_TYPES = ("SAMPLER",)
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CATEGORY = "_for_testing/custom_sampling"
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FUNCTION = "get_sampler"
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def get_sampler(self, solver_type, eta, s_noise, noise_device):
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if noise_device == 'cpu':
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sampler_name = "dpmpp_2m_sde"
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else:
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sampler_name = "dpmpp_2m_sde_gpu"
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sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})()
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return (sampler, )
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class SamplerDPMPP_SDE:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
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"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
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"r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
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"noise_device": (['gpu', 'cpu'], ),
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}
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}
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RETURN_TYPES = ("SAMPLER",)
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CATEGORY = "_for_testing/custom_sampling"
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FUNCTION = "get_sampler"
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def get_sampler(self, eta, s_noise, r, noise_device):
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if noise_device == 'cpu':
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sampler_name = "dpmpp_sde"
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else:
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sampler_name = "dpmpp_sde_gpu"
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sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})()
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return (sampler, )
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class SamplerCustom:
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@classmethod
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def INPUT_TYPES(s):
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@ -131,7 +234,12 @@ class SamplerCustom:
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NODE_CLASS_MAPPINGS = {
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"SamplerCustom": SamplerCustom,
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"KarrasScheduler": KarrasScheduler,
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"ExponentialScheduler": ExponentialScheduler,
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"PolyexponentialScheduler": PolyexponentialScheduler,
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"VPScheduler": VPScheduler,
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"KSamplerSelect": KSamplerSelect,
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"SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
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"SamplerDPMPP_SDE": SamplerDPMPP_SDE,
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"BasicScheduler": BasicScheduler,
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"SplitSigmas": SplitSigmas,
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}
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@ -331,15 +331,14 @@ class GrowMask:
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out = []
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for m in mask:
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output = m.numpy()
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while expand < 0:
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output = scipy.ndimage.grey_erosion(output, footprint=kernel)
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expand += 1
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while expand > 0:
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output = scipy.ndimage.grey_dilation(output, footprint=kernel)
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expand -= 1
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for _ in range(abs(expand)):
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if expand < 0:
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output = scipy.ndimage.grey_erosion(output, footprint=kernel)
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else:
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output = scipy.ndimage.grey_dilation(output, footprint=kernel)
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output = torch.from_numpy(output)
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out.append(output)
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return (torch.cat(out, dim=0),)
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return (torch.stack(out, dim=0),)
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31
nodes.py
31
nodes.py
@ -1781,16 +1781,23 @@ def load_custom_nodes():
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print()
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def init_custom_nodes():
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_latent.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_hypernetwork.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_rebatch.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_model_merging.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_tomesd.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_clip_sdxl.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_canny.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_freelunch.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_custom_sampler.py"))
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extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras")
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extras_files = [
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"nodes_latent.py",
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"nodes_hypernetwork.py",
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"nodes_upscale_model.py",
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"nodes_post_processing.py",
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"nodes_mask.py",
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"nodes_rebatch.py",
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"nodes_model_merging.py",
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"nodes_tomesd.py",
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"nodes_clip_sdxl.py",
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"nodes_canny.py",
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"nodes_freelunch.py",
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"nodes_custom_sampler.py"
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]
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for node_file in extras_files:
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load_custom_node(os.path.join(extras_dir, node_file))
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load_custom_nodes()
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@ -413,7 +413,11 @@ class PromptServer():
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async def get_object_info(request):
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out = {}
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for x in nodes.NODE_CLASS_MAPPINGS:
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out[x] = node_info(x)
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try:
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out[x] = node_info(x)
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except Exception as e:
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print(f"[ERROR] An error occurred while retrieving information for the '{x}' node.", file=sys.stderr)
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traceback.print_exc()
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return web.json_response(out)
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@routes.get("/object_info/{node_class}")
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Block a user